2nd Leipzig Geotechnical Symposium LeiGS2026

The Institute of Geotechnical Engineering at HTWK Leipzig and the Forschungs- und Transferzentrum are delighted to welcome you to LeiGS2026! 

LeiGS2026: November 12, 2026 - November 13, 2026

In three sessions, we will cover everything from robust data foundations to digital implementation in geotechnical engineering. Session 1 focuses on the collection and quality assurance of measurement data in the field, on construction equipment, and in the laboratory, as well as plausibility checks in connection with geotechnical parameters. Session 2 highlights new potential through the revision of Eurocode 7 and user-friendly machine learning methods: from physically informed modeling, performance evaluation, and validation to surrogate modeling and reliability-based approaches for large data sets. Session 3 will showcase practical applications of digital planning tools—in particular BIM, digital twins, and Structural Health Monitoring (SHM)—and present consistent, efficient workflows from data acquisition to processing and design to documentation.
 
We look forward to seeing you again in Leipzig in 2026 for interesting presentations, a great trade exhibition, and fruitful discussions on this topic!

MAIN FOCUS

Session 1 Methods for recording and quality assurance of geotechnical data

• Measurements and measurement methods in geotechnical engineering
• Innovative laboratory methods
  Data fusion and real-time capable systems
• Data-driven methods for determining representive values
• Automatic logging and data control 

Session 2 Methods for data-based interpretation of geotechnical data

• Algorithms for parameter determination and evaluation of tabular, spatial, and temporally distributed data
• Reliability-based design
• Spatial interpretation of field data
• Simulation, surrogate modeling, and inverse modeling

Session 3 Best practice: Digital tools in planning, execution, and monitoring

• Integration and use of building information modeling (BIM)
• Digital twins and structural health monitoring (SHM)
• Digital tools for data processing and process control
• Use of large language models (LLM) for reporting

Accepted contributions will be published in open access on the Qucosa publication server.